Platform Architecture
Deterministic Clinical Evidence Extraction
A four-step pipeline that reads unstructured medical documentation and returns source-cited, audit-ready clinical evidence — in under two minutes per document.
The Problem
The documentation review bottleneck
The friction between provider submission and payer review creates a massive documentation bottleneck. Providers spend countless administrative hours compiling clinical evidence to meet Medicare, Medicaid, and commercial claim requirements.
Federal reviewers and payers spend hours manually reading those same unstructured records to extract the relevant evidence. Our platform automates this step — at the point of submission or review.
The clinical judgment remains human. The data extraction becomes automated.
Sources
- Progress notes
- Pathology reports
- Hospice certifications
- C-CDA / HL7
- Scanned documents
CloudAnalytics Engine
- 01
Extract
Clinical points extracted with confidence scores
- 02
Cite
Document, section, and paragraph reference
- 03
Map
Against specific LCD/NCD criteria
- 04
Flag
Met, not met, or missing evidence
Output
- Source-cited evidence
- Criteria determination
- Gap identification
- Audit-ready record
Clinical judgment remains with the human reviewer.
Before
Manual chart review · Hours per record
After
Structured evidence in < 2 minutes
Pipeline
From document to determination
Ingest
PDFs, scanned documents, HL7, C-CDA, and faxed clinical notes via FHIR API or direct upload. Zero new burden on existing workflows.
Extract
Diagnoses, lab values, medications, procedures, and functional status — each with a confidence score and citation to the exact source location.
Validate
Evidence mapped against relevant clinical criteria: coverage requirements, risk adjustment standards, hospice eligibility, or PA rules.
Review
Structured evidence with source citations linked to the original document. All outputs advisory — clinical judgment stays with the reviewer.
All processing on HIPAA-compliant AWS infrastructure. PHI de-identification occurs before language model inference. Full audit trail on every extraction.
Sample Output
What a reviewer actually sees
Illustrative finding — all patient, provider, and facility identifiers redacted.
Document
Hospice Face-to-Face Certification
Provider
[REDACTED]
Date of Service
[REDACTED]
Extracted In
87 seconds
Billed Condition
Oxygen dependency — patient requires continuous supplemental O₂
NOT SUPPORTED BY DOCUMENTATION
Clinical record contradicts the billed condition. No supplemental oxygen in use at time of certification.
Source Citation
Progress Note · Page 3 · Paragraph 2
"Patient ambulates independently to bathroom without assistance. O₂ saturation 98% on room air. No supplemental oxygen in use at time of visit."
LCD Criteria
L33393 — Section 4.b.ii (Oxygen Dependency)
Determination
Not Met
Confidence
0.96
All outputs advisory. Reviewer sign-off required before any determination is recorded.
Infrastructure
Built for federal-grade security requirements
Secure Cloud Processing
Processing runs on HIPAA-compliant AWS infrastructure. PHI de-identification is enforced before any inference — eliminating PHI exposure by pipeline design. No outbound calls to external AI providers.
FHIR R4 Native
Standards-compliant interoperability with any FHIR-capable system. Direct connectivity to Epic, Cerner, athenahealth, and CMS data sources. X12 278/835, NCPDP SCRIPT, LOINC, SNOMED CT.
PHI De-identification
All 18 HIPAA identifiers stripped on-boundary before any inference. LLM endpoints only receive scrubbed clinical text. Enforced by processing topology, not configuration.
Request platform architecture documentation
Deployment diagrams, FHIR payload schemas, and AWS GovCloud reference architecture available on request.